We have developed a tow test setup for the reproducible measurement of the
dynamic properties of different types of tethered membrane wings. The test
procedure is based on repeatable automated maneuvers with the entire kite
system under realistic conditions. By measuring line forces and line angles,
we determine the aerodynamic coefficients and lift-to-drag ratio as functions
of the length ratio between power and steering lines. This nondimensional
parameter characterizes the angle of attack of the wing and is varied
automatically by the control unit on the towed test bench. During each towing
run, several test cycles are executed such that mean values can be determined
and errors can be minimized. We can conclude from this study that an
objective measurement of specific dynamic properties of highly flexible
membrane wings is feasible. The presented tow test method is suitable for
quantitatively assessing and comparing different wing designs. The method
represents an essential milestone for the development and characterization of
tethered membrane wings as well as for the validation and improvement of
simulation models. On the basis of this work, more complex maneuvers and a
full degree of automation can be implemented in subsequent work. It can also
be used for aerodynamic parameter identification.

With the turn of the millennium, kitesurfing has evolved into a mainstream
water sport, followed by the more recent variants of land and snow kiting
(Tauber and Moroder, 2013). In terms of industrial applications, flexible membrane
wings have already been used since the 1970s as aerodynamic decelerators for
airdrop systems and are currently being explored for airborne wind energy
(AWE) generation (Schmehl, 2018). Despite the advancements within the
kitesurfing and AWE industries, tethered membrane wings are mostly still
designed by iterative testing with empirical and intuitive variation of wing
parameters. Although this has led to a relatively high degree of maturity on
the product level, the approach is time-consuming and expensive because a large
number of prototypes need to be manufactured and tested. For this reason, we
conclude that the empirical design method will only allow for limited further
improvements and that it is indispensable to develop a systematic
understanding of how wing performance parameters, such as aerodynamic
coefficients, lift-to-drag ratio, steering forces and moments, depend on the
wing design.

The empirical design method is used because compared to rigid wings the
physics of flexible membrane wings are complex and the existing knowledge is
limited due to deforming under aerodynamic load and steering line actuation.
This holds particularly for leading edge inflatable (LEI) tube kites (see
Fig. 1) and other single-skin kite types, since ram air wings
have already been investigated systematically for several decades
(Dunker, 2013, 2018; Johari et al., 2014).

Because of the high degree of flexibility and the low weight of the membrane
structure, the flow around the wing and its shape are strongly coupled. A
change in the flow field alters the aerodynamic load distribution to which
the structure rapidly adjusts by deformation, which in turn changes the flow
field. The fluid–structure coupling causes deformation phenomena at different
length scales and timescales (Leuthold, 2015). A typical large-scale
phenomenon is the spanwise bending and twisting of the entire wing due to
steering line actuation. The ability of the membrane wing to deform
asymmetrically and thereby generate a substantially increased turning moment
makes it particularly suitable for AWE applications, which require excellent
maneuverability (Breukels et al., 2013; Bosch et al., 2014; van Reijen, 2018; Fechner and Schmehl, 2018).
Typical small-scale phenomena are the local flutter of the wing canopy or
wrinkling, which is caused by local compression loads that cannot be
supported by the woven fabric material.

Another characteristic that distinguishes flexible membrane wings from rigid
wings is that the entire airborne system, consisting of a wing, tensile
support system and in some cases also a suspended airborne control unit, is
considerably larger for comparable traction force. This is due to the fact
that a rigid wing can endure a much higher wing loading than a membrane wing
and that it uses aerodynamic control surfaces with wing-integrated actuators
that allow for a more compact design. For wind tunnel measurements large
geometries are typically downscaled to fit into the test section of the
tunnel. To ensure that the flow field is not affected by the scaling, the
principle of dynamic similarity has to be enforced by maintaining a constant
Reynolds number: Re=ρvc/μ. A common method to
compensate for a decreasing chord length c is to increase the flow velocity
v. However, downscaling a tethered membrane wing for wind tunnel testing
is problematic because due to aeroelasticity the aerodynamic characteristics
depend not only on the wing geometry but also on its deformation behavior. To
account for this, the material properties of the wing and tether would have
to be scaled accordingly, which is practically not feasible because the
membrane is a woven fabric material that is partially arranged as a
multilayer composite with rigid reinforcements, and the tether is a braided
and coated line (Bosman et al., 2013).

A wind tunnel study of a small but full-scale ram air wing was presented by
de Wachter (2008). The wing with a projected area of 5.2 m2 was
suspended upside down in the test sections of two different large wind
tunnels to determine the shape under aerodynamic load by photogrammetry and
laser scanning. This shape was then used as a static boundary condition for
steady CFD analysis, with the aim of assessing the computational prediction
quality without the added complexity of the deforming membrane structure. The
study contributed important knowledge about ram air wings at the lower end of
the size range. In the same framework project, Bungart (2009) performed
a coupled CFD and finite-element analysis of a ram air wing section, deriving
aerodynamic coefficients and a deformed shape for the entire range of angle of
attack. The analysis showed that the chambered design (chambers are separated
by ribs, top skin and bottom skin; see Fig. 1) with upper and
lower skin and the airfoil defined by a small number of ribs (connecting
top skin and bottom skin) leads to ballooning. A similar effect can be observed
with LEI tube kites, for which the canopy bulges out between the struts
(inflatable tube providing structure) and, similar to the ribs, defines the
design shape. It is obvious that these aeroelastic phenomena have to be
taken into account by high-fidelity analyses. Subsequently,
Breukels (2011) developed a multibody model and Bosch et al. (2014) a
finite-element model of the flexible wing, bridle line system (line system
that supports the wing structure and merges these lines into steering or
power lines) and tether. In both approaches the same correlation derived by
parametric CFD analysis is used to evaluate the aerodynamic load distribution
as a function of angle of attack and wing deformation. While succeeding in
simulating complete flight maneuvers relevant for AWE, the two studies did
not include validations by wind tunnel experiments. It can be concluded that
validated aeroelastic models of entire tethered membrane wings are neither
available at present nor sufficiently fast to be used in the
design process for which rapid iterations are required.

For this reason, less complex simulation models have been developed,
describing the whole kite system as a point mass, a cluster of point masses
(Fechner et al., 2015) or a rigid body (de Groot et al., 2011; Gohl and Luchsinger, 2013). These
models do not explicitly describe the aeroelastic behavior of the wing and
require as input the detailed aerodynamic properties of the kite system,
including information about the steering behavior. In this respect,
Erhard and Strauch (2013a, b), Fagiano et al. (2014), and Jehle and Schmehl (2014) have
proposed empirical turn rate laws relating the turn rate of the wing to the
steering input. The transition from a powered state (high angle of attack) to
depowered state (low angle of attack) is covered by an empirical correlation
(Fechner et al., 2015). According to Fagiano and Marks (2015), such lower-complexity models have already reached a quite mature state, but new insights
appear to be difficult without experimental analysis.

However, despite the strong need for reproducible experimental data, only a few
dedicated studies have been performed so far. Stevenson (2003)
developed a tow test method to support the research and development of surf
kites. The constant relative airflow was generated by driving the towing
vehicle along a beach section. The data acquisition system recorded the
lift-to-drag ratio as well as the lift coefficient both as functions of the
ratio of the sum of steering line forces to total tensile force. Inspired by
a method described by Stevenson et al. (2005), a simple stationary test setup
for the beach was used by van der Vlugt (2010) to determine the lift-to-drag
ratio of surf kites from the achievable flight speed when performing
crosswind sweeps close to the ground. Dadd et al. (2010) described a tow test
with the measurement rig mounted on a trailer such that it could be used for
stationary and tow testing. A tow test experiment for
the characterization of kites used as part of an AWE system was described by
Costa (2011). Next to the movement of the kite and the line forces,
the deformation was also measured using an image correlation system. Within the
same framework project, Wood et al. (2017) presented a control strategy for
flying figure-eight crosswind maneuvers during tow tests.

In none of the outlined test procedures was the manual control input
recorded. However, for systematic aerodynamic parameter identification a
recording of the steering inputs is crucial (de Groot et al., 2011; Mulder et al., 1994).
We started the project TETA at TU Berlin with the aim of measuring the dynamic
properties of kites under reproducible conditions for repeatable steering
input (Hummel, 2017; Hummel and Göhlich, 2017). The developed test setup is suitable
for the quantitative assessment of different types of tethered membrane wings and
can be used stationary or moving at variable velocity to simulate different
wind speeds as well as to reduce the influence of gusts.

This paper is organized as follows. Section 2 outlines the
measurement concept and describes the detailed properties to be measured. In
Sect. 3 the setup and design of the test bench is described,
including the required sensor equipment. Section 4
continues with a brief overview of the data acquisition process. In
Sect. 5 the experimental results are presented and
discussed. In the conclusions, future research and improvements of the
measurement concept and the implemented test bench are outlined.

The resultant force acts in the center of pressure of the wing. A steady
towing state is reached when the wing is no longer moving relative to the
towing vehicle. In this state, the aerodynamic and gravitational forces
acting on the wing are balanced by the tensile forces FPL,
FSL,l and FSL,r acting in the power and
steering lines. Because flexible lines cannot support bending loads these
tensile forces are always aligned with the lines, as illustrated in
Fig. 3.

The dashed line in Fig. 2 defines the tensile axis of the
airborne system, which in the case of a negligible effect of gravity is aligned
with the resultant force FR and inclined to the horizontal
plane by the elevation angle ϑ.

As illustrated in Fig. 3, the power line is attached to the
towing point at the moving test rig. The flight behavior of the wing is
controlled by a bar that can slide along the power line and attaches at its
ends to the two steering lines. This setup is commonly used for kitesurfing
and allows for the individual actuation of the left and right steering lines and
changing the effective length of the power line. The effective length of the
power line is defined as the distance between the kite attachment point and
the control bar. For the “linear power” maneuver the control bar is
automatically retracted along the power line. During this maneuver the
effective length lPL of the power line changes from
lPL,0 for the depowered state to lPL,1 for the
powered state, as illustrated in Fig. 4.

which varies between up=0 for the depowered state and
up=1 for the powered state. A similar nondimensional variable, the
relative depower setting ud=1-up, was introduced by
Fechner et al. (2015) to quantify the actuation of an airborne control unit
suspended below the wing.

In the following we describe the wing properties that are used to
characterize the flight dynamic behavior of the wing. Because the primary
objective of the study was to achieve repeatable and reproducible
measurements, we did not post-process the measured data further, for example,
to account for line sag and the influence of weight. Since kites of the
same size and the same control bar settings were tested at the same wind
speed, a relative comparison of the wings is still possible.

2.1 Aerodynamic coefficients

The aerodynamic coefficients are nondimensional parameters that describe the
aerodynamic properties of a wing. For a steady towing situation as
illustrated in Fig. 2 we can determine the lift, drag and
resultant aerodynamic coefficients of the entire system as

where ρ is the air density, A the surface area of the wing and
va the apparent wind velocity. By definition the aerodynamic drag
is aligned with the apparent wind velocity, and the aerodynamic lift is
perpendicular.

Based on the resultant aerodynamic force coefficient we can determine the
depower capability of the wing. This parameter can be calculated as the relative
difference of maximum and minimum aerodynamic forces with

(5)γ=CR,max-CR,minCR,max,

thus evaluating the entire range 1>up>0. For ground-generation AWE
systems it is the traction force of the kite that is converted into
electricity (Schmehl et al., 2013). For this variant of the technology, the
kite is generally operated in consecutive pumping cycles and for maximizing
the energy output, the resultant force coefficient CR has to be
maximized during the traction phases and minimized during the retraction
phases. For a flexible membrane wing, a good depower capability and flight
stability are two conflicting design drivers (van der Vlugt et al., 2013).

2.2 Aerodynamic efficiency

The aerodynamic efficiency of a wing can be expressed as the ratio between
the aerodynamic lift and drag force components. For a steady towing situation
as illustrated in Fig. 2 the lift-to-drag ratio can be
calculated from the elevation angle ϑ as

2.3 Tether forces

The tensile forces acting in the power and steering lines are shown in
Fig. 3. The ratio of the steering line forces to the power line
force,

(7)f=FSL,l+FSL,rFPL,

characterizes the load distribution between the rear and front parts of the
tethered wing, which allows for the validation of simulation approaches.
Additionally, to characterize sport kites this parameter has so far been used
intuitively to describe the perceived steering forces. Hence, a quantitative
comparison of different wings regarding the load distribution between power
and steering lines is feasible.

The following section gives a brief overview of the developed test bench. The
main design goals are as follows: (1) using the entire kite system (including
the unscaled kite and tether as well as the common steering input device) to
generate realistic measurement data; (2) providing constant and controllable
flow conditions; (3) allowing for repeatable and automated steering inputs;
(4) permitting as little as possible of an impairment to the wing and its control unit
by attachments; and (5) ensuring an easy transport and tow of the test bench. The
final version of the test bench is shown in Fig. 5 and the
schematic principle is illustrated in Fig. 6.

3.1 Structural design

With regard to the acquisition costs of the towed platform, a permanent
mounting on a car trailer was decided. This solution allows us to use any given
car for towing and thereby avoid additional costs. However, in contrast to
heavier vehicles (e.g., four-wheeled vehicles with a driver's cab), the
influence of oscillations into the test bench by the tethers is expected.
This results in an additional requirement for the design of the test bench.
All components are connected in such a way that it is possible to change the
driving platform in the future to further improvements. For example,
vibrations induced by the single-axle trailer could be greatly reduced by
mounting the test rig on a heavier platform.

The basic frame is used to mount the test bench modules and absorb the load,
in particular the line forces. It is assembled from aluminum profiles to
avoid corrosion and easily afford subsequent design modifications. The kite
is connected to the test bench by the pivot unit, which is located in the
rear of the trailer (in relation to the direction of travel).

The pivot unit is shown in Fig. 7. It is designed to have a
minimum inertia, which allows for a smooth untwisting of the lines. This leads to
an automatic alignment of the line connection points towards the direction of
the power line and thus towards the direction of the wing within the wind
window. The required torque for untwisting is realized by the tensile force
acting on the power line. The steering lines of the test bench are connected
to the ends of the control bar and passed through the center of the rotary
axle to realize minimal inertia. They are redirected by pulleys connected to
rope drums that are operated by motors (see Fig. 5, steering
units). The tether forces are measured by means of load cells in the steering
lines, not interconnecting the lines. A magnetic sensor attached to the
static part measures the rotation of a magnetic ring and thus of the unit
itself. The rotary part essentially consists of the rotary axle. The
universal joint is attached to it, transmitting the force of the power line.

Each steering unit, which controls the length of a steering line, consists of
a cable drum, a gearbox and a motor. The motors are each operated by a servo
controller located within the measurement and control cabinet. The steering
units are located in the middle of the test bench, together with the
batteries. Since the motors and batteries are the heaviest components of the test
rig, this arrangement allows the center of gravity to be close to the wheel
axis to prevent a static tilting of the trailer (unavoidable tilting of the
trailer is measured by an inertial sensor to correct the elevation angle
described in Sect. 3.2). The design force was set to
5000 N. In the front area in the direction of travel, space was provided
for the control cabinets.

3.2 Sensor systems

This section gives a brief overview of the sensor technology used to achieve
the measuring results, which are described in Sect. 5.
Components are termed as a sensor system, which serves the purpose of
determining certain measuring variables and for which a clear distinction
from the overall system is possible. For a complete documentation of all
sensor systems please refer to Hummel (2017).

The exact measurement of the line forces is highly prioritized due to the
requirements for the majority of kite properties (see
Sect. 2). To avoid impairments caused by additional masses
of the load cells within the steering lines, the load cells are installed
without insertion. Furthermore, this also enables the use of load cells with
a higher accuracy, which is related to a higher mass of the load cells (HBM
S2M, precision class of 0.02 %, nominal force 1000 N, which results
in an absolute error of εFS2M=±0.2 N). The resultant
forces FS2M can be obtained from Eq. (8), as
illustrated in Fig. 8, assuming that the friction of the pulley
is negligible. As shown in Eq. (8), the relation
between the force measured at the load cell and the force acting on the
steering lines is linear. This is caused by the constant line angle
βSL. With βSL=90∘ the maximum measurable
force within the steering lines is 707 N. Field tests have shown that this
value is high enough for common wing sizes. If a higher maximum force is
required in the future, the load cells can be exchanged by sensors with a
higher nominal force. However, this will be accompanied by reduced accuracy:

(8)FS2M=FSL2-2cos⁡βSL.

The measurement of the force in the power line is performed by an
interposition of the load cell (see Fig. 7). A load cell with a
nominal force of 5000 N is used, which has a precision class of
0.2 % (HBM U9C). The absolute error results in εFU9C=±10 N. The signals of the load cells are amplified and
then sent to an extension board of the sbRIO. The amplifiers are located as
shown in Fig. 5.

Measuring the angle of the power line is intended to enable a simple and
reliable determination of the elevation angle ϑ and the azimuth
angle φ, which is illustrated in Fig. 9. The polar
coordinate system, in particular the elevation angle ϑ, is based on
Erhard and Strauch (2013a). The definition of the elevation angle is suited for
determining the aerodynamic efficiency, even if the kite is not located
within the x–z plane in reference to the wind direction. In contrast to
other definitions, i.e., β in Schmehl et al. (2013), ϑ does
not vary for a constant glide ratio (see Fig. 9, intersection
of red plane with grey wind window). This angle definition facilitates the
calculation of the glide ratio even if the kite occasionally deflects from
the symmetry plane of the wind window (downwind position). The rotary axle
has a non-neglecting rotational inertia and therefore the measurement of the
azimuth and elevation angle, with respect to the test bench, is composed of
three sensors, which are shown in Fig. 7. First, the rotational
deviation within the x–y plane is calculated by the sum of the rotation
angle of the rotary axle ΦRA (measured by the magnetic sensor)
and the measured wind direction Xg. The magnetic ring of the
magnetic sensor has a sufficiently large inner diameter to pass the steering
lines through it. Thus, it is possible to mount it underneath the rotary axle
without impairing the functionality of the pivot unit. Second, the rotational
deviation of the universal joint is measured by the elevation angle sensor
(ΘUJ) and the azimuth angle sensor (ΦUJ) to
realize low friction as well as a negligible influence on the line angle. As
a result, the universal joint will already deflect at low forces in the power
line.

The wing position kw within the wind window can be
calculated by Eq. (11) as a result of the sensors; index
“g” indicates the reference to the test bench and index “w” to the wind
direction coordinate system:

Here, r represents the tether length and rPU represents the
distance between the axis of the rotary axle and the pivot point of the
universal joint (see Fig. 7). From Eq. (12) the
resulting elevation angle ϑw and azimuth angle
φw can be determined:

(12)kw=rcos⁡ϑwsin⁡φwsin⁡ϑwcos⁡φwsin⁡ϑw.

3.3 Error analysis

The error analysis of the measured data leading to the results in
Sect. 5 is described hereafter.

3.3.1 Wind speed

The absolute error of the wind speed measurement for the weather station
according to the manufacturer is εvw=0.05 m s−1. The error of the wind direction measurement
is given by εX=1∘.

For calculating the kite properties, the resulting wind speed at kite level
is needed, whereas the wind speed on top of the towing vehicle is measured.
Thus, as an additional error for the given test setup, the error due to the
height difference in wind measurement, must be investigated. The weather
station is located on top of the towing vehicle at a height zREF of
3 m. Depending on the length of the tether, the kite typically reaches a
height z of 15 to 30 m. The most commonly used extrapolation method is the
wind power law (Akdağ et al., 2013; Ghita et al., 2013). This method is assumed to be
valid within the ground-level boundary layer (<100 m). Empirical data
presented by Archer (2013) show that this model is well suited to
approximate wind profiles by measuring at a reference height zREF
and thus to estimate the wind speed vtw,plaw(z) on kite level
z. The wind power law is defined as follows:

(13)vtw,plaw(z)=vtw(zREF)zzREFα.

Here, vtw(zREF) indicates the static true wind speed at a
fixed position above the ground at an altitude zREF (index “tw”:
true wind speed), which also cannot be directly measured because of the
moving test bench. The coefficient of friction α depends on the
terrain type and increases with rising terrain roughness. Despite testing on
a former airfield, the coefficient of friction is assessed in an
overestimating way to perform a safe calculation (this overestimation will
result in an overestimated static wind speed on kite level, which in turn
will result in an overestimation of the resulting error δvw,real). Thus, it is assumed as 0.25 for wooded countryside with
many trees. If the true wind vector vtw(zREF) points
towards the opposite direction of travel, the influence of the relative error
δvw,real of the wind speed vw,real(z) at kite
level will be at a maximum. This is because the relative portion of the true
wind speed vtw(zREF) is maximized and the required speed
of the towing vehicle vp(zREF) to reach the desired
testing speed vw(zREF) is minimized:

(14)vp(zREF)=vw(zREF)-vtw(zREF).

The resulting wind speed vw,real at flight altitude z is
composed of the traveling speed vp and the theoretical wind speed
according to the wind power law vtw,plaw(z), leading to

(15)vw,real(z)=vp(zREF)+vtw,plaw(z).

The resulting error is reduced with decreasing altitude, decreasing natural
wind and increasing target speed. At present, line lengths of 24 m are
used, while the minimum target speed is set to 11 m s−1. The relative
error can thus be assumed as δvw,real≤+20 %. For a
detailed calculation please refer to Hummel (2017).

3.3.2 Elevation angle

The angle sensors of the universal joint have an absolute measuring error of
εΘUJ=εΦUJ=±0.72∘,
while the magnetic sensor has an absolute measuring error of εΦRA=±0.3∘. To determine the resultant error from the
three angle sensors, the error-prone angles ϑ and φ must be
calculated analogously to Sect. 3.2. The maximum error
was determined using a MATLAB script. At first, the error-free angles were
calculated, followed by a calculation of the error-prone angles for each
angle combination. These error-prone angles result from a combination of the
minimum and maximum values, which arise due to the individual errors
mentioned before. The maximum error of the elevation angle in the coordinate
system of the test bench is εϑg=1.2∘. If
the error of the weather station εX=1∘ is added to the
error of the magnetic sensor εΦRA, the theoretical
maximum error of the elevation angle within the wind direction coordinate
system results in εϑw=2.1∘.

The quality of the analysis could be further improved by accounting for
line sag and the influence of weight. Nevertheless, as mentioned in
Sect. 2, we did not post-process the measured data because
the primary objective of the study was the repeatability and reproducibility
of the measurements.

3.4 User interface

The developed user interface (bar stand) allows us to manipulate the control
bar position of the test bench. The pilot also receives a haptic feedback of
the line forces via the interface. The system was designed based on the
assumption that an increase in safety and reliability is achieved through an
improved perception of the prevailing flight condition, when a fully or
semi-manual flight is performed. The pilot should be able to estimate the
line forces without numerical display elements to extend the pilot's
perception of the flight situation. As a result, this device allows for the
subjective evaluation of the kite properties.

The user interface is located inside the towing vehicle and equipped with a
common control bar used to control sport kites (see Fig. 10).
The measured line forces are induced to the lines of the user interface by
means of winches operated by servomotors. The force acting on the power line
is transferred to the pilot via a harness used for kitesurfing. The motor
position and thus the current bar position is determined by integrated
encoders. This setup enables a control of the wing, which is close to
reality, by moving a common control bar as well as by transmitting the scaled
forces acting on the lines. The maximum force of the steering lines was set
to 50 N and the force of the power line to 350 N. This determination was
made to avoid a physical overstressing of the pilot and to limit the size of
the actuators. The measured line forces must therefore be scaled by a
proportionality factor.

The visual feedback is realized by the display shown in Fig. 10.
Because of the integration of the user interface into the towing vehicle a
direct view of the wing is impossible. The image is taken by means of a
wide-angle camera atop the roof of the vehicle. To enable a subsequent video
evaluation, the recorded data are stored on the camera's internal memory
card. When the measurement procedure is started by the pilot, video recording
is initiated automatically by the sbRIO (central control unit; see
Sect. 4.1). An LED is placed within the visual
range of the camera for the later synchronization of the video and the
measured data. This enables the synchronization of the beginning of data
recording with the beginning of the video.

To record the measurement data acquired from the sbRIO and perform
control inputs to set up the test run, a notebook is used as a host computer.
The host computer communicates with the sbRIO via network interface. During a
test run, the notebook is placed in front of the pilot so that a perception
of the numerical display elements of the host computer is possible. During a
test procedure the pilot is not required to execute inputs on the host
computer.

Furthermore, a foot pedal connected to the host computer is used to
execute maneuvers in the testing mode. When the pedal is actuated by the
pilot, the previously set maneuver is executed by the sbRIO. Depending on the
degree of automation, the pilot is enabled to act out certain steering inputs
via the control bar. As soon as the pilot releases the pedal, the maneuver is
terminated and the kite can be controlled manually again.

4.1 Data processing system

This section briefly describes the structure of the data processing hardware of the
test bench. A schematic diagram is shown in Fig. 11.
The data processing system and the DC power supply are localized within
the measurement and control cabinet (see Fig. 5). As shown in
Fig. 11, the National Instruments sbRIO 9632 serves as the
central control unit. It is connected to various components, such as sensors,
via a self-made custom interface board. The servo controllers of the motors
mentioned in Sect. 3.1 communicate via a CAN module
with the sbRIO. A network interface is used to communicate with the host
computer and retrieve measured values of the spherical camera array.
The sbRIO has been chosen because of the implemented central processing unit
(CPU) and the field-programmable gate array (FPGA).

The CPU allows the main control algorithm (the real-time operating system;
RTOS) to be executed in real-time. To ensure a safe test operation, a
real-time capability is required. In particular, control inputs have to be
executed in a predefined time. For this purpose, a deterministic loop was
introduced within the RTOS (with a maximum execution period of 20 ms). This
allows the motors to be addressed at a frequency of 50 Hz. The FPGA
processor, on the one hand, is used as an access to the analog and digital
interfaces via the internal bus of the sbRIO. On the other hand, programs can
be implemented that are converted into a logic circuit by means of the
integrated gates. Due to the configurable logic circuit, parallel signal
processing is possible, which increases the speed of the data processing.

4.2 Experimental setup

The dynamic test procedure used in this paper is described below. Dynamic
tests are characterized by moving the test bench. The procedure can be
carried out on any straight track. It is of paramount importance that the
ground surface is as flat as possible to reduce oscillations. The
measurements within this work have been carried out on the former airport
Pütnitz, Germany. The target wind speed was consistently set to 22 kn
(11.3 m s−1) to demonstrate the repeatability of the test method.

The range of wind speed that can be examined is only limited by the cut-in
wind speed of the kite (minimum wind speed for flying the kite) and the
maximum tensile force resulting from the kite acting on the test bench (the
design force was set to 5000 N; see Sect. 3.1).
Because of the weight of the test bench the maximum vertical force is
currently limited to 3000 N, which could be increased by using a heavier
trailer. Assuming a coefficient of CR=0.7 (representing the peak
value in Fig. 15), surface area of A=10 m2, air density of
ρ=1.184 kg m−3 and apparent wind velocity of
va=50 kt (25.7 m s−1), the resulting force is
FR=2837 N <3000 N (see Eq. 4). Since the aerodynamic
coefficients investigated so far are wind independent, there is no need to
test in higher wind speeds to compare the wings against each other. For the
presented maneuver “linear power” in combination with the presented wing
sizes, a maximum testing speed of 50 kn (25.7 m s−1) can be given.
The traction force will increase substantially when the kite is operated in
crosswind maneuvers (Schmehl et al., 2013). To analyze kites in this flight
mode with traction forces exceeding 5000 N, the design of the test bench has
to be adapted or the surface area of the wing has to be reduced accordingly.

Figure 12 shows the towing vehicle with the test bench in
measuring operation. Measurements are solely conducted on the straight
sections. As described above, tests are carried out on days with as little
wind as possible. Testing under these conditions allows for the performance of
multiple maneuvers without landing the kite since the track can be run both
ways.

To launch the kite, it is set up behind the towing vehicle, placed on its
trailing edge and the lines are tightened. When accelerating the test bench, the kite
does an ascent movement in the direction of the zenith. The driver of the
vehicle is supplied with a display showing the duplicated view of the host
computer. That way, the driver can assess the current flight situation and the
currently measured wind speed. The driver adjusts the desired wind speed via
the cruise control of the towing vehicle. After reaching the target speed,
the maneuvers can be carried out.

4.3 Measurement data evaluation

The measurement data are evaluated by means of the software Diadem, which is
originated by the company National Instruments, also supplying the software
for the host as well as the measurement and control unit.

The implemented script is used to preprocess, process and display the
measurement data. First, the desired measurement files are transferred to the
script. Then, each measurement file is preprocessed in a loop. This includes,
among other functions, an automatic detection of maneuvers and a distinction
between driving along the straight track and turnaround. To obtain the
desired graphs, statistical values are calculated from the maneuvers. The
graphs and an overview of the measured data are then added to a report
PDF for each measurement file. Once each measurement file has been processed,
the results are summarized in an additional overview to allow for a comparison
between each file.

This section presents the obtained results for the wing properties defined in
Sect. 2. The measurements were taken by means of
the maneuver “linear power” to demonstrate the functionality of the test
bench and the feasibility of the developed test procedure. Before starting
the maneuver, the wing is positioned and stabilized by the pilot at the
zenith position within the wind window. The foot pedal connected to the host
computer is then manually actuated to launch the maneuver. The power position
is automatically increased by the sbRIO up to ΔlPL,max=500 mm (see Fig. 4) with a constant speed
over a period of 4.5 s. The pilot can still execute steering inputs to keep
the kite in a stable position at the zenith.

The measurement diagrams are shown in the following subsections. Only
maneuvers lasting a given minimal time span were taken into account. During
some maneuvers an unintentional change in position or orientation (e.g.,
caused by gusts) led to the pilot aborting the maneuver; this can result in
a too-short maneuver, which in turn would make the statistical value
calculation impossible. The valid results are plotted against the power ratio
up. The determination of the angle of attack was not feasible
within the scope of this work and will be done in future research for this
project.

5.1 Tested kites

For characterization of the dynamic properties, five different kites with the
same surface area of 10 m2 were measured (denoted by kite A to E within
the graphs). All kites are designed for different purposes in kite sports.

On the one hand, kite C was designed to ride efficiently upwind, i.e.,
affording a high traveling angle in the wind direction. In addition, high jumps
with a long air time should be possible. Therefore, a high aerodynamic
efficiency associated with a high resulting force is required. Furthermore,
this kite should provide a high depower capability, resulting in a
significant change in the lift coefficient.

Kites D and E have the same design, but originated from different model
years. Because of their shape, these kites feature a significant contrast to
the other kites. Significantly more wing area is located at the wing tips,
which should result in lower aerodynamic efficiency and a lower lift
coefficient.

Kite A is intended to be an all-rounder, which means the resulting lift
and efficiency should be positioned between C and D–E.

Kite B is designed to achieve good handling and turning abilities as well as
providing a good upwind performance at the same time. For this reason the
steering forces have to be higher while depowered (up≃0)
compared to the other kites.

The measurements were conducted during two different days (marked as day 1
and 2). For each property, a figure is shown that summarizes all measurement
data into a single curve for each kite to compare the kites against each
other. Additionally, these figures show the resulting error from
all maneuvers that were taken into account for a confidence interval of
95 %.

5.2 Aerodynamic efficiency

The measurement results of the elevation angle ϑw can be
seen in Fig. 13. The resulting aerodynamic efficiency can be
calculated by Eq. (6) (see Fig. 14). The
different curves can be distinguished by height and progression.

As discussed in the previous chapter, it can be shown that kite C offers the
highest and kites D–E the lowest aerodynamic efficiency. It can also be
concluded that a reliable repeatability within the same day can be achieved. This
finding was confirmed by further tests on different days. The only
significant deviation was found after a long period between two test runs.
The time between day 1 and day 2 was exactly 1 year. The elevation angle
differs between these days only by an offset. To determine this offset in the
future and, if necessary, to compensate for it, a reference kite was introduced,
which is measured once every test day. The resulting curves of this reference
kite should fit each other on different test runs. If an offset occurs, the
starting points of the graphs can be corrected and thus the wings can still
be compared relatively to each other. To fully compensate for this deviation in
the future, the initial horizontal alignment of the test bench will be
measured by means of an inertial measurement unit. The deviation is most
likely caused by changes in geometry being difficult to control, for example
a change in the tire pressure of the trailer or the towing vehicle.

5.3 Lift coefficient

The lift coefficient CL is calculated according to Eq. (2)
using the given manufacturer's surface area of 10 m2 and a constant air
density of ρ=1.184 kg m−3. The airflow velocity is assumed as
equal to the measured wind speed of the weather station. The total tether force
is calculated by the sum of the measured forces of three load cells. Due to
the high elevation angles, the resulting force coefficient CR
resembles CL and is not shown separately (see Eqs. 4
and 2 with sin⁡(ϑw>70∘)≈1).

The resulting curves of the datasets are shown in Fig. 15. As
predicted in Sect. 5.1, kite C offers the highest and kites
D–E the lowest lift coefficient. The deviation between datasets for the same
kite lies within the resulting error. The influence of the abovementioned
deviation of the elevation angle measurement on the lift coefficient is
negligible.

As mentioned in Sect. 2.1, the depower
capability for each kite can be calculated by the difference between the
maximum and minimum values. Apparently, kites B and C are best suited for AWE
systems using the pumping mode because of their high depower capability and their high lift coefficient. A further distinction can be made based
on the curve progressions. Kites A to C can be characterized by their
degressive progression, whereas kites D and E are characterized by a
progressive increase in the lift coefficient.

To estimate the reproducibility for each kite property, all eight
datasets are first presented together within the same diagram
(Fig. 16). Obviously, a distinction between the kites is
possible. As a result of their different wing shapes, the curve progression
of kites D and E compared to the other kites is clearly different
(progressive). Furthermore, the kites can be distinguished by the height of the
force ratio. With these curves and the force curve itself, existing
simulation models can be evaluated reliably.

For AWE systems the force ratio is of great importance, since it determines
the steering possibility of the wing while fully depowered (especially during
the retraction phase). To guarantee the execution of control commands by
transmitting the steering forces, the force ratio must not be too
low.

In most cases, a reproducible, high-quality measurement of the flight dynamic
properties of tethered flexible membrane wings exceeds the available budget.
Furthermore, existing approaches do not allow for a recording or even
automation of steering inputs, which is crucial for the reproducibility of
the experiment. In this paper, we have presented a unique tow test setup for
automatic measurement of the dynamic properties of different wing types at
full scale and under realistic conditions. The objective was to demonstrate
the methodology and particularly the repeatability of the test procedure.
Using the maneuver “linear power”, we determine the aerodynamic
coefficients and lift-to-drag ratio of the wing as functions of the ratio of
power and steering line lengths – denoted as the relative power setting – by
measuring line forces and line angles. The ratio is varied automatically,
while the pilot is manually adjusting the steering line lengths to keep the
kite at a fixed position relative to the towing vehicle. By automating the
test cycles we can acquire mean values of high statistical quality with
minimal errors. We have demonstrated repeatability on the basis of eight
recorded datasets using the maneuver “linear power” at a constant wind
speed of 22 kn (11.3 m s−1). We conclude from this study that it is
feasible to objectively measure the flight dynamic properties of tethered
membrane wings and to quantitatively assess and compare different wing
designs.

Based on this work, we propose several functional enhancements for future
research. By performing more sophisticated flight maneuvers the full
operational envelope of airborne wind energy systems can be covered. By
completing the automation of the process we expect a significant increase in
measurement accuracy, which will improve the future aerodynamic parameter
identification and evaluation of existing simulation models. A further
accuracy increase can be achieved by adding sensors to the wing and directly measuring
the flight state and the relative flow.

Roland Schmehl has received funding from the European Union Horizon 2020
research and innovation program under Marie Skłodowska-Curie grant
agreement no. 642682 for the ITN project AWESCO and grant agreement no.
691173 for the “Fast Track to Innovation” project REACH.

We describe a tow test setup for the reproducible measurement of aerodynamic, structural dynamic and flight dynamic properties of tethered membrane wings. The test procedure is based on repeatable automated maneuvers with the entire kite system under realistic conditions. The developed measurement method can be used to quantitatively compare different wing designs, to validate and improve simulation models, and to systematically improve kite designs.

We describe a tow test setup for the reproducible measurement of aerodynamic, structural dynamic...